SoroushVahidi/scidocs huggingface dataset:
Minimum mean square error detector for multimessage spread spectrum embedding
(2009)

In this paper, a new minimum mean square error (MMSE) based detector is presented for multi-message detection in both conventional spread spectrum (SS) watermarking and improved spread spectrum (ISS) watermarking. The proposed detector yields an improved detection performance in terms of bit error rate (BER) for both SS and ISS multiple message embedding. Simulation results demonstrate that the proposed MMSE detector outperforms the conventional detector, the matched filter detector, by orders of magnitude.
Dissociation Following Traumatic Stress Etiology and Treatment
(2010)

We postulate that the cascade ‘‘Freeze-Flight-Fight-Fright-Flag-Faint’’ is a coherent sequence of six fear responses that escalate as a function of defense possibilities and proximity to danger during life-threat. The actual sequence of trauma-related response dispositions acted out in an extremely dangerous situation therefore depends on the appraisal of the threat by the organism in relation to her/his own power to act (e.g., age and gender) as well as the perceived characteristics of threat and perpetrator. These reaction patterns provide optimal adaption for particular stages of imminence. Subsequent to the traumatic threats, portions of the experience may be replayed. The actual individual cascade of defense stages a survivor has gone through during the traumatic event will repeat itself every time the fear network, which has evolved peritraumatically, is activated again (i.e., through internal or external triggers or, e.g., during exposure therapy).When a parasympathetically dominated ‘‘shutdown’’ was the prominent peri-traumatic response during the traumatic incident, comparable dissociative responses may dominate responding to subsequently experienced threat and may also reappear when the traumatic memory is reactivated. Repeated experience of traumatic stress forms a fear network that can become pathologically detached from contextual cues such as time and location of the danger, a condition which manifests itself as posttraumatic stress disorder (PTSD). Intrusions, for example, can therefore be understood as repetitive displays of fragments of the event, which would then, depending on the dominant physiological response during the threat, elicit a corresponding combination of hyperarousal and dissociation. We suggest that trauma treatment must therefore differentiate between patients on two dimensions: those with peritraumatic sympathetic activation versus those who went down the whole defense cascade, which leads to parasympathetic dominance during the trauma and a corresponding replay of physiological and dissociative responding, when reminded. The differential management of dissociative stages (‘‘fright’’ and ‘‘faint’’) has important treatment implications.
SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems
(2013)

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommender's trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.
Deep Learning meets Semantic Web: A feasibility study with the Cardiovascular Disease Ontology and PubMed citations
(2016)

Background: Automatic identification of gene and protein names from biomedical publications can help curators and researchers to keep up with the findings published in the scientific literature. As of today, this is a challenging task related to information retrieval, and in the realm of Big Data Analytics. Objectives: To investigate the feasibility of using word embed-dings (i.e. distributed word representations) from Deep Learning algorithms together with terms from the Cardiovascular Disease Ontology (CVDO) as a step to identifying omics information encoded in the biomedical literature. Methods: Word embeddings were generated using the neural language models CBOW and Skip-gram with an input of more than 14 million PubMed citations (titles and abstracts) corresponding to articles published between 2000 and 2016. Then the abstracts of selected papers from the sysVASC systematic review were manually annotated with gene/protein names. We set up two experiments that used the word embeddings to produce term variants for gene/protein names: the first experiment used the terms manually annotated from the papers; the second experiment enriched/expanded the annotated terms using terms from the human-readable labels of key classes (gene/proteins) from the CVDO ontology. CVDO is formalised in the W3C Web Ontology Language (OWL) and contains 172,121 UniProt Knowledgebase protein classes related to human and 86,792 UniProtKB protein classes related to mouse. The hypothesis is that by enriching the original annotated terms, a better context is provided, and therefore, it is easier to obtain suitable (full and/or partial) term variants for gene/protein names from word embed-dings. Results: From the papers manually annotated, a list of 107 terms (gene/protein names) was acquired. As part of the word embeddings generated from CBOW and Skip-gram, a lexicon with more than 9 million terms was created. Using the cosine similarity metric, a list of the 12 top-ranked terms was generated from word embeddings for query terms present in the generated lexicon. Domain experts evaluated a total of 1968 pairs of terms and classified the retrieved terms as: TV (term variant); PTV (partial term variant); and NTV (non term variant, meaning none of the previous two categories). In experiment I, Skip-gram finds the double amount of (full and/or partial) term variants for gene/protein names as compared with CBOW. Using Skip-gram, the weighted Cohen's Kappa inter-annotator agreement for two domain experts was 0.80 for the first experiment and 0.74 for the second experiment. In the first experiment, suitable (full and/or partial) term variants were found …
A computational approach to motion perception
(2004)

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.
A novel booster antenna on flexible substrates for metal proximity NFC applications
(2015)

In this article a novel booster antenna is proposed in order to increase the read range of a small (1.1×10-4 m2) metal mounted HF (13.56 MHz) RFID. The highly inductive coupling between the reader and booster, and between the RFID and booster is the main propagation method. The communication between the reader and RFID realized through the booster antenna. For this purpose two coils are connected in parallel to each other resonating at the same frequency. The greater coil is coupled to the reader antenna and the smaller coil is coupled to the RFID. The simulation and measurement results show the reliability of the proposed structure.
Microbial co-operation in the rhizosphere.
(2005)

Soil microbial populations are immersed in a framework of interactions known to affect plant fitness and soil quality. They are involved in fundamental activities that ensure the stability and productivity of both agricultural systems and natural ecosystems. Strategic and applied research has demonstrated that certain co-operative microbial activities can be exploited, as a low-input biotechnology, to help sustainable, environmentally-friendly, agro-technological practices. Much research is addressed at improving understanding of the diversity, dynamics, and significance of rhizosphere microbial populations and their co-operative activities. An analysis of the co-operative microbial activities known to affect plant development is the general aim of this review. In particular, this article summarizes and discusses significant aspects of this general topic, including (i) the analysis of the key activities carried out by the diverse trophic and functional groups of micro-organisms involved in co-operative rhizosphere interactions; (ii) a critical discussion of the direct microbe-microbe interactions which results in processes benefiting sustainable agro-ecosystem development; and (iii) beneficial microbial interactions involving arbuscular mycorrhiza, the omnipresent fungus-plant beneficial symbiosis. The trends of this thematic area will be outlined, from molecular biology and ecophysiological issues to the biotechnological developments for integrated management, to indicate where research is needed in the future.
Facade Segmentation in the Wild
(2018)

Urban façade segmentation from automatically acquired imagery, in contrast to traditional image segmentation, poses several unique challenges. 360◦ photospheres captured from vehicles are an effective way to capture a large number of images, but this data presents difficult-to-model warping and stitching artifacts. In addition, each pixel can belong to multiple façade elements, and different facade elements (e.g., window, balcony, sill, etc.) are correlated and vary wildly in their characteristics. In this paper, we propose three network architectures of varying complexity to achieve multilabel semantic segmentation of façade images while exploiting their unique characteristics. Specifically, we propose a MULTIFACSEGNET architecture to assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank formulation that encourages extraction of rectangular elements, and a COMPATIBILITY network that simultaneously seeks segmentation across facade element types allowing the network to ‘see’ intermediate output probabilities of the various façade element classes. Our results on benchmark datasets show significant improvements over existing façade segmentation approaches for the typical façade elements. For example, on one commonly used dataset the accuracy scores for window (the most important architectural element) increases from 0.91 to 0.97 percent compared to the best competing method, and comparable improvements on other element types.
Using Big Data as a window into consumers’ psychology
(2017)

The rise of ‘Big Data’ had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information – with the help of new analytical techniques – can be translated into valuable insights on consumers’ psychological states and traits that can, in turn, be used to inform marketing strategy. Finally, we discuss opportunities and challenges related to the use of Big Data as a window into consumers’ psychology, and provide recommendations for how to implement related technologies in a way that benefits both businesses and consumers.
Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture
(2018)

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.
Memory training impacts short-term changes in aging white matter: a longitudinal diffusion tensor imaging study.
(2012)

A growing body of research indicates benefits of cognitive training in older adults, but the neuronal mechanisms underlying the effect of cognitive intervention remains largely unexplored. Neuroimaging methods are sensitive to subtle changes in brain structure and show potential for enhancing our understanding of both aging- and training-related neuronal plasticity. Specifically, studies using diffusion tensor imaging (DTI) suggest substantial changes in white matter (WM) in aging, but it is not known whether cognitive training might modulate these structural alterations. We used tract-based spatial statistics (TBSS) optimized for longitudinal analysis to delineate the effects of 8 weeks intensive memory training on WM microstructure. 41 participants (mean age 61 years) matched for age, sex and education were randomly assigned to an intervention or control group. All participants underwent MRI-scanning and neuropsychological assessments at the beginning and end of the study. Longitudinal analysis across groups revealed significant increase in frontal mean diffusivity (MD), indicating that DTI is sensitive to WM structural alterations over a 10-week interval. Further, group analysis demonstrated positive effects of training on the short-term changes. Participants in the training group showed a relative increase in fractional anisotropy (FA) compared with controls. Further, a significant relationship between memory improvement and change in FA was found, suggesting a possible functional significance of the reported changes. The training effect on FA seemed to be driven by a relative decrease in radial diffusivity, which might indicate a role for myelin-related processes in WM plasticity.
Anatomy of GPU Memory System for Multi-Application Execution
(2015)

As GPUs make headway in the computing landscape spanning mobile platforms, supercomputers, cloud and virtual desktop platforms, supporting concurrent execution of multiple applications in GPUs becomes essential for unlocking their full potential. However, unlike CPUs, multi-application execution in GPUs is little explored. In this paper, we study the memory system of GPUs in a concurrently executing multi-application environment. We first present an analytical performance model for many-threaded architectures and show that the common use of misses-per-kilo-instruction (MPKI) as a proxy for performance is not accurate without considering the bandwidth usage of applications. We characterize the memory interference of applications and discuss the limitations of existing memory schedulers in mitigating this interference. We extend the analytical model to multiple applications and identify the key metrics to control various performance metrics. We conduct extensive simulations using an enhanced version of GPGPU-Sim targeted for concurrently executing multiple applications, and show that memory scheduling decisions based on MPKI and bandwidth information are more effective in enhancing throughput compared to the traditional FR-FCFS and the recently proposed RR FR-FCFS policies.
Application-aware prioritization mechanisms for on-chip networks
(2009)

Network-on-Chips (NoCs) are likely to become a critical shared resource in future many-core processors. The challenge is to develop policies and mechanisms that enable multiple applications to efficiently and fairly share the network, to improve system performance. Existing local packet scheduling policies in the routers fail to fully achieve this goal, because they treat every packet equally, regardless of which application issued the packet.
 This paper proposes prioritization policies and architectural extensions to NoC routers that improve the overall application-level throughput, while ensuring fairness in the network. Our prioritization policies are application-aware, distinguishing applications based on the stall-time criticality of their packets. The idea is to divide processor execution time into phases, rank applications within a phase based on stall-time criticality, and have all routers in the network prioritize packets based on their applications' ranks. Our scheme also includes techniques that ensure starvation freedom and enable the enforcement of system-level application priorities.
 We evaluate the proposed prioritization policies on a 64-core CMP with an 8x8 mesh NoC, using a suite of 35 diverse applications. For a representative set of case studies, our proposed policy increases average system throughput by 25.6% over age-based arbitration and 18.4% over round-robin arbitration. Averaged over 96 randomly-generated multiprogrammed workload mixes, the proposed policy improves system throughput by 9.1% over the best existing prioritization policy, while also reducing application-level unfairness.
Project Adam: Building an Efficient and Scalable Deep Learning Training System
(2014)

Large deep neural network models have recently demonstrated state-of-the-art accuracy on hard visual recognition tasks. Unfortunately such models are extremely time consuming to train and require large amount of compute cycles. We describe the design and implementation of a distributed system called Adam comprised of commodity server machines to train such models that exhibits world-class performance, scaling and task accuracy on visual recognition tasks. Adam achieves high efficiency and scalability through whole system co-design that optimizes and balances workload computation and communication. We exploit asynchrony throughout the system to improve performance and show that it additionally improves the accuracy of trained models. Adam is significantly more efficient and scalable than was previously thought possible and used 30x fewer machines to train a large 2 billion connection model to 2x higher accuracy in comparable time on the ImageNet 22,000 category image classification task than the system that previously held the record for this benchmark. We also show that task accuracy improves with larger models. Our results provide compelling evidence that a distributed systems-driven approach to deep learning using current training algorithms is worth pursuing.
TunePad: Computational Thinking Through Sound Composition
(2017)

Computational thinking skills will be important for the next generation of students. However, there is a disparity in the populations joining the field. Integrating computational thinking into artistic fields has shown to increase participation in computer science. In this paper, we present our initial design prototype for TunePad, a sound composition tablet application controlled by a block-based programming environment. TunePad is designed to introduce learners to computational thinking and to prepare them for text-based coding environments. From our preliminary testing, with children ages 7-14, we observed that our design actively engages learners and communicates how the programming blocks control the sounds being played. This testing is a prelude to more formal studies as we continue to improve the design and interface of TunePad. With this work, we intend to engage students in computational thinking who may not have otherwise been exposed, giving the opportunity to more people to enter the computer science field.
Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server
(2018)

With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.
BareDroid: Large-Scale Analysis of Android Apps on Real Devices
(2015)

To protect Android users, researchers have been analyzing unknown, potentially-malicious applications by using systems based on emulators, such as the Google's Bouncer and Andrubis. Emulators are the go-to choice because of their convenience: they can scale horizontally over multiple hosts, and can be reverted to a known, clean state in a matter of seconds. Emulators, however, are fundamentally different from real devices, and previous research has shown how it is possible to automatically develop heuristics to identify an emulated environment, ranging from simple flag checks and unrealistic sensor input, to fingerprinting the hypervisor's handling of basic blocks of instructions. Aware of this aspect, malware authors are starting to exploit this fundamental weakness to evade current detection systems. Unfortunately, analyzing apps directly on bare metal at scale has been so far unfeasible, because the time to restore a device to a clean snapshot is prohibitive: with the same budget, one can analyze an order of magnitude less apps on a physical device than on an emulator.
 In this paper, we propose BareDroid, a system that makes bare-metal analysis of Android apps feasible by quickly restoring real devices to a clean snapshot. We show how BareDroid is not detected as an emulated analysis environment by emulator-aware malware or by heuristics from prior research, allowing BareDroid to observe more potentially malicious activity generated by apps. Moreover, we provide a cost analysis, which shows that replacing emulators with BareDroid requires a financial investment of less than twice the cost of the servers that would be running the emulators. Finally, we release BareDroid as an open source project, in the hope it can be useful to other researchers to strengthen their analysis systems.